Jos Miguel Hernndez-Lobato, University of CambridgeProf. Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . The workshop is being organized by application area or other, panels, invited speakers, interactive, small groups, discussions, presentations. While original contributions are preferred, we also invite submissions of high-quality work that has recently been published in other venues or is concurrently submitted. Held in conjunction with KDD'22 Aug 15, 2022 - Washington DC, USA. The workshop will focus on two thrusts: 1) Exploring how we can leverage recent advances in RL methods to improve state-of-the-art technology for ED; 2) Identifying unique challenges in ED that can help nurture technical innovations and next breakthroughs in RL. With this in mind, we welcome relevant contributions on the following (and related) topic areas: The submissions must be in PDF format, written in English, and formatted according to the AAAI camera-ready style. ACM Transactions on Spatial Algorithms and Systems (TSAS), accepted. This workshop aims to bring together researchers from AI and diverse science/engineering communities to achieve the following goals: 1) Identify and understand the challenges in applying AI to specific science and engineering problems2) Develop, adapt, and refine AI tools for novel problem settings and challenges3) Community-building and education to encourage collaboration between AI researchers and domain area experts. 963-971, Apr-May 2015. Following this AAAI conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. As deep learning problems become increasingly complex, network sizes must increase and other architectural decisions become critical to success. The workshop is a full day. DI-2022 accepted papers will not be archived in the main KDD 2022 proceedings. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. Although textual data is prevalent in a large amount of finance-related business problems, we also encourage submissions of studies or applications pertinent to finance using other types of unstructured data such as financial transactions, sensors, mobile devices, satellites, social media, etc. We would especially like to highlight approaches that are qualitatively different from some popular but computationally intensive NAS methods. 11, 2022: We have posted the list of accepted Workshops at, Apr. Instead of grading each piece of work individually, which can take up a bulk of extra time, intelligent scoring tools allow teachers the ability to have their students work automatically graded. The main interest of the proposed workshop is to look at a new perspective of system engineering where multiple disciplines such as AI and safety engineering are viewed as a larger whole, while considering ethical and legal issues, in order to build trustable intelligent autonomy. Technology has transformed over the last few years, turning from futuristic ideas into todays reality. iDetective: An Intelligent System for Automatic Identification of Key Actors in Online Hack Forums. Methods for learning network architecture during training, including Incrementally building neural networks during training, new performance benchmarks for the above. (Depending on the volume of submissions, we may be able to accommodate only a subset of them.). Invited speakers, committee members, authors of the research paper, and the participants of the shared task are invited to attend. These cookies ensure basic functionalities and security features of the website, anonymously. Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features. Furthermore, leveraging AI to connect disparate social networks amongst teachers \\cite{karimi2020towards}, we may be able to provide greater resources for their planning, which have been shown to significantly affect students achievement. Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. Workshop registration is available to AAAI-22 technical registrants at a discounted rate, or separately to workshop only registrants. Application-specific designs for explainable AI, e.g., healthcare, autonomous driving, etc. Andrew White, University of RochesterDr. Disentangled Spatiotemporal Graph Generative Model. Babies learn their first language through listening, talking, and interacting with adults. DeepGAR: Deep Graph Learning for Analogical Reasoning. However, ML systems may be non-deterministic; they may re-use high-quality implementations of ML algorithms; and, the semantics of models they produce may be incomprehensible. Onn Shehory, Bar Ilan University (onn.shehory@biu.ac.il), Eitan Farchi, IBM Research Haifa (farchi@il.ibm.com), Guy Barash, Western Digital (Guy.Barash@wdc.com), Supplemental workshop site:https://sites.google.com/view/edsmls-2022/home. Kyoto . In decision-making domains as wide-ranging as medication adherence, vaccination uptakes, college enrollment, retirement savings, and energy consumption, behavioral interventions have been shown to encourage people towards making better choices. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. SDU accepts both long (8 pages including references) and short (4 pages including references) papers. Public health authorities and researchers collect data from many sources and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors. Oral presentations: 10 minute presentation for oral papers. Are you sure you want to create this branch? Data mining systems and platforms, and their efficiency, scalability, security and privacy. The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. Its capabilities have expanded from processing structured data (e.g. There will be about 60~85 people to participate, including the program committee, invited speakers, panelists, authors of accepted papers, winners of the competition and other interested people. "The EMBERS architecture for streaming predictive analytics." Mitigating Cache-Based Side-Channel Attacks through Randomization: A Comprehensive System and Architecture Level Analysis. The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, network theory, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with a focus on applications in population and personalized health. arXiv preprint arXiv:2207.09542 (2022). 4. Long talks (50 mins):Gabriel Peyr, (Mathematics, CNRS Senior Researcher);Yusu Wang, (Mathematics, Professor in CSE, UCSD);Caroline Uhler, (Statistics and CS, Associate Professor in EECS and IDSS, MIT); Short talks (25mins):Titouan Vayer, (Mathematics, Postdoctoral Researcher at ENS Lyon);Tam Le, (Computer Science, Research Scientist at RIKEN);Dixin Luo, (Computer Science, Assistant Professor in CS, Beijing Institute of Technology). Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. The workshop will be a one-day workshop, featuring speakers, panelists, and poster presenters from machine learning, biomedical informatics, natural language processing, statistics, behavior science. We hope this will help bring the communities of data mining and visualization more closely connected. Note: This is the inaugural event of a conference dedicated to Graph Machine Learning. KDD 2022 CPM: A General Feature Dependency Pattern Mining Framework for Contrast Multivariate Time Series. We invite submissions to the AAAI-22 workshop on Graphs and more Complex structures for Learning and Reasoning to be held virtually on February 28 or March 1, 2022. We expect 50-65 people in the workshop. As Artificial Intelligence (AI) begins to impact our everyday lives, industry, government, and society with tangible consequences, it becomes increasingly important for a user to understand the reasons and models underlying an AI-enabled systems decisions and recommendations. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. Junxiang Wang, Liang Zhao, Yanfang Ye, and Yuji Zhang. Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment. For previous workshops held physically, each workshop attracts around 150~300 participants. : Papers are submitted through the CMT portal for this workshop: Please select the track for your submission in Primary Subject Area and indicate if your submission is a full paper or an extended abstract in Secondary Subject Area. We welcome full research papers, position papers, and extended abstracts. In light of these issues, and the ever-increasing pervasiveness of AI in the real world, we seek to provide a focused venue for academic and industry researchers and practitioners to discuss research challenges and solutions associated with building AI systems under data scarcity and/or bias. In addition to the keynote and presentations of accepted works, the workshop will include both a general discussion session on defining and addressing the key challenges in this area , and a lightning tutorial session that will include brief overviews and demos of relevant tools, including open source frameworks such as Ecole. It is difficult to expose false claims before they create a lot of damage. However, these real-world applications typically translate to problem domains where it is extremely challenging to even obtain raw data, let alone annotated data. This workshop brings together researchers from diverse backgrounds with different perspectives to discuss languages, formalisms and representations that are appropriate for combining learning and reasoning. The research contributions may discuss technical challenges of reading and interpreting business documents and present research results. Everyone in the Top-10 leaderboard submissions will have a guaranteed opportunity for an in-person oral/poster presentation. Geographical Mapping and Visual Analytics for Health Data, Biomedical Ontologies, Terminologies, and Standards, Bayesian Networks and Reasoning under Uncertainty, Temporal and Spatial Representation and Reasoning, Crowdsourcing and Collective Intelligence, Risk Assessment, Trust, Ethics, Privacy, and Security, Computational Behavioral/Cognitive Modeling, Health Intervention Design, Modeling and Evaluation, Applications in Epidemiology and Surveillance (e.g., Bioterrorism, Participatory Surveillance, Syndromic Surveillance, Population Screening), Hybrid methods, combining data driven and predictive forward models, biomedical signal analysis/modeling (EEG, ECG, PPG, EMG, fMRI, IMU, medical/clinical data, etc. The third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22) builds on the success of previous years PPAI-20 and PPAI-21 to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. ACM Transactions on Spatial Algorithms and Systems (TSAS), 5, 3, Article 19 (September 2019), 28 pages. Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, and Liang Zhao. Novel AI-based techniques to improve modeling of engineering systems. The study of complex graphs is a highly interdisciplinary field that aims to study complex systems by using mathematical models, physical laws, inference and learning algorithms, etc. [Best Paper Candidate]. Merge remote-tracking branch 'origin/master', 2. Any participant who experiences unacceptable behavior may contact any current member of the SIGMOD Executive Committee, the PODS Executive Committee, DBCares, or this year's D&I co-chairs Pnar Tzn (pito@itu.dk) and Renata Borovica-Gajic (renata.borovica@unimelb.edu.au). Social Media based Simulation Models for Understanding Disease Dynamics. Knowledge Discovery and Data Mining is an interdisciplinary area focusing Identification of information-theoretic quantities relevant for causal inference and discovery. In this 2nd instance of GCLR (Graphs and more Complex structures for Learning and Reasoning) workshop, we will focus on various complex structures along with inference and learning algorithms for these structures. Track 1 covers the issues and algorithms pertinent to general online marketplaces as well as specific problems and applications arising from those diverse domains, such as ridesharing, online retail, food delivery, house rental, real estate, and more. There were two workshops on similar topics hosted at ICML 2020 and NeurIPS 2020, and both workshops observed positive feedback and overwhelming participation. 3434-3440, Melbourne, Australia, Aug 2017. This manual extraction process is usually inefficient, error-prone, and inconsistent. . AAAI is pleased to present the AAAI-22 Workshop Program. AI is now shaping the way businesses, governments, and educational institutions do things and is making its way into classrooms, schools and districts across many countries. These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. World Wide Web Conference (WWW 2018), (acceptance rate: 14.8%), Lyon, FR, Apr 2018, accepted. Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework. It has gained popularity in some domains such as image classification, speech recognition, smart city, and healthcare. Despite the great success of deep neural networks (DNNs) in many artificial intelligence (AI) tasks, they still suffer from limitations, such as poor generalization behavior for out-of-distribution (OOD) data, vulnerability to adversarial examples, and the black-box nature of DNNs. The VTU workshops accepts both short paper (4 pages) and long paper (8 pages). The final schedule will be available in November. Some of the key questions to be explored include: The workshop will take place in person and will span over one day. Xuchao Zhang, Liang Zhao, Zhiqian Chen, and Chang-Tien Lu. 4 pages), and position (max. The invited speakers, who are well-recognized experts of the field, will give a 30 minute talk. Dataset(s) will be provided to hack-a-thon participants. References will not count towards the page limit. The AAAI author kit can be downloaded from:https://www.aaai.org/Publications/Templates/AuthorKit22.zip. The 39th IEEE International Conference on Data Engineering (ICDE 2023), accepted. Attendance is open to all. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. Gabriel Pedroza (CEA LIST), Jos Hernndez-Orallo (Universitat Politcnica de Valncia, Spain), Xin Cynthia Chen (University of Hong Kong, China), Xiaowei Huang (University of Liverpool, UK), Huascar Espinoza (KDT JU, Belgium), Mauricio Castillo-Effen (Lockheed Martin, USA), Sen higeartaigh (University of Cambridge, UK), Richard Mallah (Future of Life Institute, USA), John McDermid (University of York, UK), Supplemental workshop site:http://safeaiw.org/. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. The submissions need to be anonymized. 2022. We allow both short (2-4 pages) and long papers (6-8 pages) papers. Jan 13, 2022: Notification. 2022. Template guidelines are here:https://www.acm.org/publications/proceedings-template. Knowledge discovery from various data sources has gained the attention of many practitioners in recent decades. Workshop URL:https://rail.fzu.edu.cn/info/1014/1064.htm, Prof. Chi-Hua ChenEmail: chihua0826@gmail.comPostal address: No.2, Xueyuan Rd., Fuzhou, Fujian, ChinaTelephone: +86-18359183858. Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao. New self-supervised proxy tasks or new approaches using self-supervised models in speech and audio processing. Objectives of ADAM include outlining the main research challenges in this area, cross-pollinating collaborations between AI researchers and domain experts in engineering design and manufacturing, and sketching open problems of common interest. The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains. And considering robustness, input data with noises frequently occur in open-world scenarios, which presents critical challenges for the building of robust AI systems in practice. Topics of interest include, but are not limited to: Paper submissions will be in two formats: full paper (8 pages) and position paper (4 pages): The submission website ishttps://easychair.org/conferences/?conf=trase2022. Accepted contributions will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals. The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2021), (acceptance rate: 23.6%), accepted. Ting Hua, Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. 47, no. Submissions will be collected via the OpenReview platform; URL forthcoming on the Workshop website. Note: The workshop is a collaboration between NASSMA organisation, Deepmind and UM6P. Deep Graph Spectral Evolution Networks for Graph Topological Evolution. All submissions must be anonymous and conform to AAAI standard for double-blind review. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities. Checklist for Revising a SIGKDD Data Mining Paper, How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering, https://researcher.watson.ibm.com/researcher/view_group.php?id=144, IEEE International Conference on Big Data (, AAAI Conference on Artificial Intelligence (, IEEE International Conference on Data Engineering (, SIAM International Conference on Data Mining (, Pacific-Asia Conference on Knowledge Discovery and Data Mining (, ACM SIGKDD International Conference on Knowledge discovery and data mining (, European Conference on Machine learning and knowledge discovery in databases (, ACM International Conference on Information and Knowledge Management (, IEEE International Conference on Data Mining (, ACM International Conference on Web Search and Data Mining (, 18.4% (181/983, research track), 22.5% (112/497, applied data science track), 59.1% (107/181, research track), 35.7% (40/112, applied data science track), 17.4% (130/748, research track), 22.0% (86/390, applied data science track), 49.2% (64/130, research track), 41.9% (36/86, applied data science track), 18.1% (142/784, research track), 19.9% (66/331, applied data science track), 49.3% (70/142, research track), 60.1% (40/66, applied data science track), 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper), 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper), 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper), 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper), 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research), 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper), Short papers are presented at poster sessions, 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper), 23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages), 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track), 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular), 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular), 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular), 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^, 16.3% (84/514 in which 3 papers are withdrawn/rejected after the acceptance), 28.4% (23/81, long presentation), 71.6% (58/81, short presentation)^, 30% (24/80, long presentation), 70% (56/80, short presentation)^, 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^, 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^.